An architecture for real-time scoring with R

March 1, 2019
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Let's say you've developed a predictive model in R, and you want to embed predictions (scores) from that model into another application (like a mobile or Web app, or some automated service). If you expect a heavy load of requests, R running on a single server isn't going to cut it: you'll need some kind of distributed architecture with enough servers to handle the volume of requests in real time.

This reference architecture for real-time scoring with R, published in Microsoft Docs, describes a Kubernetes-based system to distribute the load to R sessions running in containers. This diagram from the article provides a good overview:

Realtime-scoring-r-architecture

You can find detailed instructions for deploying this architecture in Github. This architecture uses Azure-specific components, but you could also use their open source equivalents if you wanted to host them yourself:

For more details on this architecture, take a look at the Microsoft Docs article linked below.

Microsoft Docs: Real-time scoring of R machine learning models

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